Parties whose content is viewed by users of digital devices frequently desire to know the demographics of the audience by whom their content is being viewed. The process of identifying the audience that views particular content is referred to “audience verification”. Audience verification is particularly important when, for example, the content is an advertisement and the nature of the audience that views the advertisement affects how much the advertiser needs to pay for the advertisement. For example, a baby food manufacturer may pay more when their advertisement is viewed primarily by females between the ages of 20 and 40 than when their advertisement is viewed primarily by males between the ages of 5 and 15.
Current industry measurement systems for digital audience verification are used to help quantify and audit the total delivery of media for advertising campaigns. These systems generally use a census or near census dataset on media delivery (either cookie level data or portal data such as Facebook) to calibrate an opt-in consumer panel with known stated demographics (age, gender, income, etc.).
While these existing audience verification systems have helped move measurement forward from traditional modeled demographic approaches, a key gap in accurate verification of demographics remains due to the issue of co-usage of devices. Specifically, current audience verification systems often operate under the assumption that the person that is using a digital device to view content is the same person whose profile is associated with that device. Such device-to-profile associations may be established using cookies, or through other means. Unfortunately, the person using a device is often not the person whose profile is associated with the device.
Devices are often shared or borrowed. When a first user borrows a second user's device, current systems often treat content delivered to the device while the device is being used by first user as having been viewed by the second user. If the first user's demographics are significantly different than the second user's demographics (as in the case where children are using a parent's device), the accuracy of the audience verification statistics decreases.
Current systems use reported panelist demographics coming from the panelist at the time the panelists join a panel, and are typically the demographics of the owner of the device. For the purposes of explanation, the term “owner” is used herein to designate the person whose profile information is associated with the device. Such a person may not actually own the device if, for example, the device is a work-provided computer.
Since consumers can share devices, the actual demographics of the consumer in front of the screen at the time of advertisement exposure can differ from the original demographics for the owner of the device (or original panelist associated with the device at the time of joining the panel).
Using the demographics associated with a consumer authentication, such as a portal login id, can help reduce errors caused by users sharing devices. For example, if a user has signed in to a particular account on a particular social network, then it is generally safe to assume that, within the domain of that social network, the demographic information associated with that account accurately reflects the demographics of the person using the device (even though those demographics may be different from the demographics of the owner of the device). However, for content presented from outside of that specific domain, the problem remains. Specifically, it is not clear whether the person using a device to view content from site X is (a) the person that used the device to sign in to site Y, (b) the person that owns the device, or (c) someone else. In such cases, an audience authentication system that assumes that the viewer is the person that signed into site Y will sometimes be wrong. Similarly, an audience authentication system that assumes that the viewer is the owner of the device will sometimes be wrong.
The approaches described in this section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section.